Online stator and rotor resistance estimation scheme using swarm intelligence for induction motor drive in EV/HEV
Bibliographic record
Abstract
The usage of niche copper-rotor induction motor (CRIM) in the Tesla Roadster electric vehicle has bolstered the technology of using copper-rotor induction motor for electrified transportation. Understanding the merits, demerits and state of art technology of induction motor and its drive in EV/HEV application, this research manuscript proposes an online stator and rotor resistance estimation scheme using particle swarm optimization (PSO) technique for efficient and accurate control of induction motors in the same application. Firstly, an insight is provided on the state or art CRIM technology in EV/HEV and the need for reliable online rotor and stator resistance estimation scheme. Secondly, a PSO based scheme for resistance estimation is developed through a mathematical model. The developed model is validated and tested on a 10hp CRIM thorough a computer programme. Thereafter, the calculated results obtained from numerical investigations are analyzed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".